BEYOND THE BINARY: DECONSTRUCTING A MONOLINGUAL VS. MULTILINGUAL OPPOSITION IN ENGLISH LANGUAGE TEACHING
Bibliographic record
Abstract
A monolingual vs. multilingual opposition has come to the forefront in recent language education research. Here, immersion theory argues for exclusive exposure to and use of the target language in curricular spaces (Lambert & Tucker, 1972; Ballinger et al., 2017), while plurilingual (Beacco & Byram, 2007; Piccardo et al., 2021) and translanguaging scholars (Garcia, 2009; Lewis et al., 2012) contend that language learning should leverage the complex interconnections and composite nature of languages. Unfortunately, in the context of English-language education, immersion theories have been mobilized by proponents of hegemonic English-only policies that result in a devaluing of learners’ first/other languages by failing to tap into the linguistic resources and identities they bring to the classroom (Cummins, 2007). This Mixed Methods research project thus consists of a double move with Derrida (1967) and deconstruction as its core. The first involves a deconstruction of the “monolingual habitus” (Gogolin, 1994) of English-Only policies, seen here as a manifestation of linguistic imperialism (Phillipson, 1992). Then in the second move, deconstruction calls for the breakdown of fixed binaries and an uncovering of the tension and complexity in questions. Applied here, a simple opposition of Mono vs. Multi fails to illuminate the complexities of what goes on with languages in the English Language Teaching classroom in-between. Statistical and thematic analyses were conducted on survey (n=125) and follow-up interview (n=9) and observation (n=5) data with post-secondary English language instructors in Japan and Canada, oriented across a Mono-Multi spectrum. Findings reveal the complexities in institutional policies and the beliefs, policies, practices, and experiences of teachers around classroom language use, showing the Mono vs. Multi opposition to be a largely theoretical construct. The findings also uncover a greater range of pedagogical options, combining plurilingual and immersion pedagogies, which can be applied at different stages of a lesson in diverse contexts. Rather than an either/or proposition then, this combination deconstructs both the hegemony of English-only policies as well as a constructed Mono vs. Multi binary, leading to ‘and/and,’ or contextually sensitive, hybrid plurimmersion pedagogies.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.015 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.007 | 0.057 |
| Scholarly communication | 0.017 | 0.021 |
| Open science | 0.001 | 0.016 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".